Automatic bot creation based on scripts

ABSTRACT

Methods and systems for transcript-based bot creation are provided. Information may be stored in memory regarding different automation templates associated with different statement types. A transcript may be imported that includes statements, which may be analyzed and classified as one or more of the different statement types. The imported transcript may be displayed in a graphic user interface with its statements displayed in accordance with the automation templates associated with the respective statement type. User input may be received, including modification input that modifies at least one automation template associated with at least one statement of the displayed transcript designated as an integration point. A custom bot may thereafter be generated based on the modification input and configured to conduct a conversation based on the imported template and to initiate a workflow at the integration point in accordance with the modified automation template.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present invention claims the priority benefit of U.S. provisionalpatent application No. 62/768,699 filed Nov. 16, 2019, the disclosure ofwhich is incorporated by reference herein.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention generally relates to bot creation. Morespecifically, the present invention relates to automatic bot creationbased on scripts.

2. Description of the Related Art

Presently, most contact center operations occur by manual means and areperformed by human agents. Such agents may be trained to answerquestions and provide answers and basic services on behalf of a specificbrand. Part of agent training may therefore include how to interact witha consumer in a conversation that reflects well on the brand. Suchconversations may therefore not only be responsive to a particularconsumer query or request, but may be informative, solves problems, andotherwise engage the consumer. Due to the training requirements ofproviding effective service, contact centers cannot scale well by justadding more people.

Meanwhile, presently available chat bots are not as effective as humanagents at engaging the consumer, discerning the precise query orrequest, and being responsive thereto. FIG. 1, for example, illustratesa number of different chat bot failures, resulting from inability toidentify precisely what the user is asking. Such failure to properly andaccurately identify the purpose of the user query or request thereafterleads to failure to respond appropriately or in a way that satisfies theuser. Further, creating a bot is generally a complex and time-intensiveengineering process performed by bot coders and developers who do nothave firsthand knowledge or training in contact center operations. Sucha process is likewise not scalable, and the resulting bots may lack theengagement ability of human agents.

There is, therefore, a need in the art for improved systems and methodsfor intelligence-driven automatic bot creation based on scripts andagent interaction.

SUMMARY OF THE CLAIMED INVENTION

Embodiments of the present invention include methods and systems fortranscript-based bot creation. Information may be stored in memoryregarding a set of different automation templates associated withdifferent statement types. A transcript may be imported that includes aplurality of statements, which may be analyzed to classify thestatements as one or more of the different statement types. The importedtranscript may be displayed in a graphic user interface with itsstatements displayed in accordance with the automation templatesassociated with the respective statement type. User input may bereceived, including modification input that modifies at least oneautomation template associated with at least one statement of thedisplayed transcript. Designated as an integration point. A custom botmay thereafter be generated based on the modification input andconfigured to conduct a conversation based on the imported template andto initiate a workflow at the integration point in accordance with theat least one modified automation template.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a number of different chat bot failures, resultingfrom inability to identify precisely what the user is asking.

FIG. 2 illustrates an exemplary successful conversation with aconsumer/user and analysis thereof.

FIG. 3A-3F illustrate a variety of dashboards that provide differentanalyses of statements and conversations.

FIG. 4 is a flowchart that illustrates an exemplary method for automatedbot creation.

FIG. 5 is a diagram illustrating an exemplary system architecture forautomated bot creation.

FIG. 6A-6D are diagrams illustrating exemplary analytic processesperformed during certain steps in automated bot creation.

FIGS. 7A-7E illustrate exemplary graphic user interfaces (GUIs)providing customization tools by which a customized bot may begenerated.

DETAILED DESCRIPTION

Embodiments of the present invention provide for intelligence-drivenautomatic bot creation based on scripts and agent interaction.Conversations between consumers and agents may be tracked in real-timeand over time to discern insights as to which ones result in successfuloutcomes and which do not. Certain metrics and scores may be evaluatedin real-time and over time to evaluate which statement types result incertain actions and result in consumer satisfaction. Certain queries mayresult in connecting the consumer with billing, marketing and sales,purchasing, or customer service, for example, and certain statements mayindicate satisfaction with such results. As such, individuals withspecialized knowledge (e.g., agents regarding customer service) mayparticipate in bot creation without having to generate software code.Instead, automation templates may be intelligently identified andmatched to scripts, as well as modified (on-the-fly or coordinated),trained, refined, and optimized without the need for code.

As such, analyses between conversations between consumers and agents mayallow for better and more accurate discernment of consumer queries andrequests, as well as better and more accurate responses from agents (orbots). As used herein, intent refers to a user desire to changesomething related to a brand or business. Such change may apply to aplan type, upgrade a product, to buy a shoe, to learn about a store'sinventory for a certain product set, or to learn something about aproduct they don't understand today. From the business perspective,intent may be used as the unit of management by which contact centersinitiate certain automated actions and/or automated workflows. It's akey concept for understanding key automations needed, how to routecustomer contacts, whether and how well agents are succeeding, how tobest incentivize the agent team, etc. It also improves understand whycustomers are reaching out to the brand, as well as analyze recenttrends and customer zeitgeists. Because of this, intent recognition mayserve as the basis for intelligence-driven automated conversationbuilding, management, and analyses.

FIG. 2 illustrates an exemplary successful conversation with aconsumer/user and analysis thereof. The consumer statement may beidentified as including certain indicators of intent, specifically theintent to obtain information (e.g., hours, product lines and options,billing) regarding the business' services under certain circumstances(e.g., in Switzerland during a time period of Aug. 24-Sep. 1, 2018). Theagent thereafter successfully identified the proper result based on theintent and the applicable modifiers. The success of the conversation maybe indicated by the last user response (e.g., including praise andthanks versus criticism). In addition to the actual content of theconversation, certain other parameters and metrics may be tracked andmeasured in order to evaluate success and improve how laterconversations are designed. For example, information regarding textsize, message length, timing and duration, different icons (to indicatebot or human agent), etc., may be tracked to evaluate the relativesuccesses thereof. Sentiment scores may be assigned, for example, toquantify a degree of success of a conversation; such scores may be basedon the language and statements provided by the consume/user during theconversation.

FIG. 3A-3F illustrate a variety of dashboards that provide differentanalyses of statements and conversations. Each statement or conversationmay be associated with different intents (e.g., different actions orworkflows), as well as different measured parameters (e.g., duration,sentiment scores, trends over time). The dashboards may further includedifferent visual charts and plots based on certain selected metrics. Inaddition, filter tools may be applied by agents and other administratorsto filter by selected parameters, metrics, and agents. Such analyses andfilter tools may be applied in real-time to current conversations, aswell as a collection of past conversations, to provide insights that aidsubsequent conversation design. Such dashboards may be used by agents orother brand/business administrator to gain real-time insights acrossmultiple conversations occurring in real-time, as well as the evolvingset of historical conversational data and related metrics. The intent ofa statement may be analyzed in accordance with such analytics, whetherin real-time or a subsequent time. Such analysis may thereafter be usedto intelligently analyze agent and bot performance, as well as scriptsthereof. Such intelligence may further be used as described herein toguide creation of a customized bot.

FIG. 4 is a flowchart that illustrates an exemplary method for automatedbot creation. In step 410 of such method, certain successful chattranscripts may be identified and used as the basis for creating acustom bot capable of identifying the specific user intent andinitiating an automated workflow in response. The success may beidentified based on various analytics, including those discussed inrelation to FIGS. 3A-G.

In step 420, an agent or other administrator may select the identifiedtranscript to upload or otherwise designate a specific transcript to amodule executable to generate a customizable bot. Such transcript mayhave been identified, evaluated, scored, etc., based on intentanalytical tools and selected from a dashboard such as illustrated inFIGS. 3A-3G.

In step 430, the selected transcript(s) may therefore be analyzed toclassify the statements included therein. For example, statements madeby consumer/user may be distinguished from those made by the agent(human or bot). Further, the statements by the user may be analyzed todetect all the relevant pieces of conversation (e.g., intents,interactions, goals). Such analyses may make use of one or more naturallanguage understanding (NLU) tools that are available through thirdparties (e.g., Google, Watson, Microsoft, Amazon Lex) or a brand's ownproprietary systems.

In step 440, a display of a dialogue may be generate based on theanalysis. For example, the statements from a customer may be separatedfrom those of the agent or bot. Visual indicators may be included in agraphic user interface (GUI) to illustrate representative statements ofeach participant.

In step 450, different intents and modifier options may be detected inthe transcript and characterized by type (e.g., type of intent, type ofmodifier). In step 460, an automation (e.g., automated interaction) maybe identified based on the identified intent and characterization(s)thereof.

In steps 470A-C, the automation may be assigned, modified, and used tocreate an interaction sequence or workflow that is responsive to theidentified intent and characterization(s). In step 470A, suchcharacterization may be used to identify a responsive statement (or setof statements) as in integration point where an automated action orworkflow (collectively, automation) may be initiated in response. Suchautomations may be predefined and associated with specific types ofstatements (indicating different intents or modifiers). The automationsmay be predefined by the brand/business or may be provided through thirdparty services. Various APIs may be made available that allow forintegration with automations powered by different available and/orproprietary systems. In step 470B, the characterization(s) associatedwith the identified intent may be used as parameters to fill in slots ofinformation needed to run an automation (e.g., where intent is searchand the slots specify black shoes in size 7). In step 470C, a custominteraction sequence or automation may be generated. In step 480, theprocess may be repeated for other interaction or integration points andother intents.

FIG. 5 is a diagram illustrating an exemplary system architecture forautomated bot creation. As illustrated, the system may include a varietyof different client devices 510A-C that may be used to generate bots.Such clients may include, for example, web clients 510A, mobile clients510B, and bot clients 510C. Load balancers 520 may be used to distributenetwork and/or application traffic to various servers.

Exemplary servers may include chat servers 530, micro services 540, andbot servers 550, as well as various supporting tools 560. Asillustrated, multiple different services may be made accessible andintegrated into bot creation and operation. The resulting bot maytherefore be able to access existing services and tools in order toinitiate and conduct conversations, as well as to initiate and conductworkflows responsive to the conversations.

FIG. 6A-6D are diagrams illustrating exemplary analytic processesperformed during certain steps in automated bot creation. FIG. 6Aillustrates an exemplary blueprint for a bot, which includes dialoguesassociated with one or more interactions. Each dialogue may beidentified as associated with a particular intent, and each interactionmay include one or more entities and slots. For example, FIG. 6Billustrates a “Shoe Finder” bot entity (e.g., associated with an intentto find shoes) that conducts an exemplary dialogue. Meanwhile, thecustomer responses (e.g., “Boots” and “Black” may be used to fill in theslots associated with the “Shoe Finder” bot entity.

FIG. 6C illustrates how an artificially intelligent (AI) assist widgetmay operate to guide creation of the bot in accordance with anautomation associated with the intent and entity. For example, theillustrated AI assist widget that may assist in identifying theappropriate entity and slots in accordance with the requirements of thespecific automated interaction. FIG. 6D further illustrates certaintools, including a product find tool (“Prod_Find”) associated with aspecified image URL and item URL Such image may be retrieved for displaywhen the associated tool is initiated, and the item data may beretrieved for further display upon selection.

FIGS. 7A-7E illustrate exemplary graphic user interfaces (GUIs)providing customization tools by which a customized bot may begenerated. FIG. 7A illustrates a variety of different automationtemplates that may be available for incorporation and customization in abot. In some instances, the automation templates may be identified froma store of pre-existing templates, which may be filtered by relevanceand applicability to industry vertical (e.g., retail,telecommunications, and finance). Common automation templates mayinclude lead generation, product/service search and filter,product/service promotion, appointment scheduling, order status,frequently asked questions (FAQs), etc.

FIG. 7B illustrates a GUI that may be generated to display an importedand selected transcript following analysis and characterization of thestatements therein. The resulting GUI therefore presents an agent with adisplay of transcript statements associated with specified parametersand matched to an automation template (e.g., made available with APIintegration). Each automation template may allow for different modifiersto be selected and incorporated into the resulting automation initiatedby the custom bot. As such, new or modified automations may be createdfrom existing templates.

The GUI of FIG. 7B further includes the chat bot conversation that mayresult based on the selected and customized template. Such GUI allows anagent to further modify and tweak the conversation diagram before thecustom bot is generated. Generation of the custom bot may thereafter bebased on the imported transcript that has been selected, analyzed, andcharacterized based on real-time intelligent insights, as well as agentinput, to match certain statements with custom automations. Such bot maybe generated anew or based on pre-existing bots that are modified inaccordance with the transcript, analysis, and agent input. The resultingcustom bot therefore not only reflects the intelligence that comes fromanalyzing a growing and evolving store of previous conversations andanalyses in order to detect intent of a conversation and also detect theproper interaction needed to intelligently route to the appropriatechannel to enable a smooth conversation.

FIG. 7C illustrates the AI assist widget, which guides the agent basedon the requirements of the automated interaction. For example, the AIassist widget may assist in identifying other intents associated withthe entity, as well as the slots needed to be filled in order to run anautomated interaction. FIG. 7D illustrates alternative ways to customizea bot response. Such exemplary responses may include listed options forshoe search, as well as illustrated options. Such data may be retrievedfrom designated product databases based on the identified intent andcharacterization parameters and presented to the customer for evaluationand selection.

FIG. 7E illustrates an exemplary conversations that include a change inslot parameters. For example, one conversation includes a change from“boots” to “pumps,” while the other conversation includes a change from“black” to “leopard.” Such changes may therefore remove the prior slotparameter from the automation templates and replace with the new slotparameter provided by the customer.

In some embodiments, a conversation manager dashboard may be providedthat allows an agent to manage and further train multiple bots. Suchdashboard may be based on real-time analysis of current conversations toidentify points (e.g., where a handoff may be desired) at which to routeto certain automations. Such handoff may allow a conversation conductedby a bot to hand off to an agent, and vice versa, to provide efficientresults. Insights from the conversation manager may further be used tosupervise bot learning and improve bot-driven conversations and generalbot operation further.

AI may analyze each step of a conversation to assess consumer intent,taking available context and history into consideration. With thesecapabilities, conversations may be automatically routed to the bestautomations tuned specifically for the brand and minimize need humanintervention over time. If a conversation needs to be routed to an agentbased on real time sentiment or consumer request, all the consumer andconversation details may be made available in structured form for theagent after a warm hand-off. The consumer has to repeat nothing, and theagent instantly can see the history of the conversation. Additionally,intent analysis will be available in real-time, giving brands an instantview in the questions customers and asking and allowing them take quickaction.

Exemplary system in which automated bot creation and management may beimplemented may include a conversation builder (including GUI-basedtools for intelligent bot creation by agents), a conversation manager(bot management analytical tools), and conversation intelligence module(analytical insights into conversations). Such conversation tools maymake use of artificial intelligence and machine learning availablethrough an AI engine applied to a selected set of data. In addition tobeing built and managed intelligently, conversation tools may make useto any combination or automations that may be made available, eitherthrough proprietary systems or made available from third party serviceproviders (through APIs). Such AI engine may therefore analyze all theconversations and scans each turn for intent and next best action, whichmay enables a suite of features from smart routing between bots, agents,and automations, as well as intelligent handoffs; trend detection andreaction, analytics, recommendations, etc. Some embodiments may includean agent assist widget that recommends handoffs to certainbots/automations at certain points and handoffs from bots at otherpoints based on real-time capacity, intent, and other insights. As such,existing automation templates may be customized by an agent to enhancebranding, as well as integrate into the brand's systems or third partyservices. In addition, such agent may monitor the bot in real-time so asto flag any potential issues and to refine the bot based on the same

In addition, some of these capabilities may exist in adjacent platformsthat may be integrated and available for agents and managers to use inone cohesive experience. The resulting platform may use AI toorchestrate conversations across all messaging channels (e.g., online,websites, SMS, social media and messaging applications such as WhatsAppor Facebook Messenger) that supports integrations through a powerful setof APIs.

The present invention may be implemented in an application that may beoperable using a variety of computing devices. Non-transitorycomputer-readable storage media refer to any medium or media thatparticipate in providing instructions to a central processing unit (CPU)for execution. Such media can take many forms, including, but notlimited to, non-volatile and volatile media such as optical or magneticdisks and dynamic memory, respectively. Common forms of non-transitorycomputer-readable media include, for example, a floppy disk, a flexibledisk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROMdisk, digital video disk (DVD), any other optical medium, RAM, PROM,EPROM, a FLASHEPROM, and any other memory chip or cartridge.

Various forms of transmission media may be involved in carrying one ormore sequences of one or more instructions to a CPU for execution. A buscarries the data to system RAM, from which a CPU retrieves and executesthe instructions. The instructions received by system RAM can optionallybe stored on a fixed disk either before or after execution by a CPU.Various forms of storage may likewise be implemented as well as thenecessary network interfaces and network topologies to implement thesame.

The foregoing detailed description of the technology has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or to limit the technology to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. The described embodiments were chosen in order to best explainthe principles of the technology, its practical application, and toenable others skilled in the art to utilize the technology in variousembodiments and with various modifications as are suited to theparticular use contemplated. It is intended that the scope of thetechnology be defined by the claim.

What is claimed is:
 1. A method for transcript-based bot creation, the method comprising: storing information in memory regarding a set of different automation templates associated with different statement types; importing a transcript via a communication interface, the imported transcript comprising a plurality of statements; analyzing the imported transcript to classify the statements as one or more of the different statement types; matching the classified statements of the imported transcript to one or more of the stored automation templates associated with the respective statement type of the classified statement; displaying the imported transcript in a graphic user interface, wherein the classified statements within the transcript are displayed in association with the matching automation templates associated with the respective statement type of the classified statements; receiving user input that modifies at least one automation template associated with the displayed transcript, the modified automation template associated with one of the classified statements designated as an integration point; and generating a custom bot configured to conduct a conversation based on the imported template and to initiate a workflow at the integration point in accordance with the at least one modified automation template.
 2. The method of claim 1, wherein the set of automation templates is associated with an industry vertical, wherein a different industry vertical is associated with a different set of automation templates.
 3. The method of claim 1, wherein the different statement types include statements by agent and statements by end-user.
 4. The method of claim 1, wherein at least one of the different statement types is matched to the one or more automation templates based on a predefined intent.
 5. The method of claim 1, further comprising tracking a plurality of live conversations with an end-user in real-time, and identifying an intent associated with one of the statements in one of the live conversations, wherein classifying the statement in the imported transcript comprises a comparison to tracked live conversations and the identified intent for the tracked live conversation.
 6. The method of claim 1, further comprising identifying an intent associated with a workflow initiated by an agent associated with a live conversation.
 7. The method of claim 1, wherein analyzing the imported transcript comprises making a prediction regarding a recommended workflow to initiate in response to one or more of the statements in the transcript.
 8. The method of claim 1, wherein analyzing the imported transcript comprises identifying a handoff point between the bot and an agent associated with a live conversation.
 9. The method of claim 1, further comprising generating an agent dashboard displaying current metrics regarding tracked conversations.
 10. The method of claim 1, further comprising providing a filter tool selectable to filter a plurality of tracked conversations based on one or more of the metrics.
 11. The method of claim 1, wherein analyzing the imported transcript comprises assigning a sentiment score.
 12. A system for transcript-based bot creation, the method comprising: memory that stores information regarding a set of different automation templates associated with different statement types; a communication interface that imports a transcript comprising a plurality of statements; a processor that executes instructions stored in memory to: analyze the imported transcript to classify the statements as one or more of the different statement types; and match the classified statements of the imported transcript to one or more of the stored automation templates associated with the respective statement type of the classified statement; and a graphic user interface that: displays the imported transcript, wherein the graphic user interface displays the statements within the transcript in accordance with the automation templates associated with the respective statement type, and receives user input that modifies at least one automation template associated with the displayed transcript, the modified automation template associated with one of the statements designated as an integration point; wherein the processor executes further instructions to generate a custom bot configured to conduct a conversation based on the imported template and to initiate a workflow at the integration point in accordance with the at least one modified automation template.
 13. The system of claim 12, wherein the set of automation templates is associated with an industry vertical, wherein a different industry vertical is associated with a different set of automation templates.
 14. The system of claim 12, wherein the different statement types include statements by agent and statements by end-user.
 15. The system of claim 12, wherein at least one of the different statement types is matched to the one or more automation templates based on a predefined intent.
 16. The system of claim 12, wherein the graphic user interface further tracks a plurality of live conversations with an end-user in real-time, and wherein the processor executes further instructions to identify an intent associated with one of the statements in one of the live conversations, wherein the processor classifies the statement in the imported transcript based on a comparison to tracked live conversations and the identified intent for the tracked live conversation.
 17. The system of claim 12, wherein the processor executes further instructions to identify an intent associated with a workflow initiated by an agent associated with a live conversation.
 18. The system of claim 12, wherein the processor analyzes the imported transcript by making a prediction regarding a recommended workflow to initiate in response to one or more of the statements in the transcript.
 19. The system of claim 12, wherein the processor analyzes the imported transcript by identifying a handoff point between the bot and an agent associated with a live conversation.
 20. The system of claim 12, wherein the graphic user interface further generates an agent dashboard displaying current metrics regarding tracked conversations.
 21. The system of claim 12, wherein the graphic user interface further provides a filter tool selectable to filter a plurality of tracked conversations based on one or more of the metrics.
 22. The system of claim 12, wherein the processor analyzes the imported transcript by assigning a sentiment score.
 23. A non-transitory, computer-readable storage medium, having embodied thereon a program executable by a processor to perform method for transcript-based bot creation, the method comprising: storing information in memory regarding a set of different automation templates associated with different statement types; importing a transcript via a communication interface, the imported transcript comprising a plurality of statements; analyzing the imported transcript to classify the statements as one or more of the different statement types; matching the classified statements of the imported transcript to one or more of the stored automation templates associated with the respective statement type of the classified statement; displaying the imported transcript in a graphic user interface, wherein the classified statements within the transcript are displayed in association with the matching automation templates associated with the respective statement type of the classified statements; receiving user input that modifies at least one automation template associated with the displayed transcript, the modified automation template associated with one of the classified statements designated as an integration point; and generating a custom bot configured to conduct a conversation based on the imported template and to initiate a workflow at the integration point in accordance with the at least one modified automation template.
 24. The non-transitory, computer-readable storage medium of claim 23, wherein the set of automation templates is associated with an industry vertical, wherein a different industry vertical is associated with a different set of automation templates.
 25. The non-transitory, computer-readable storage medium of claim 23, wherein the different statement types include statements by agent and statements by end-user.
 26. The non-transitory, computer-readable storage medium of claim 23, wherein at least one of the different statement types is matched to the one or more automation templates based on a predefined intent.
 27. The non-transitory, computer-readable storage medium of claim 23, further comprising instructions executable to track a plurality of live conversations with an end-user in real-time, and to identify an intent associated with one of the statements in one of the live conversations, wherein classifying the statement in the imported transcript comprises a comparison to tracked live conversations and the identified intent for the tracked live conversation.
 28. The non-transitory, computer-readable storage medium of claim 23, further comprising instructions executable to identify an intent associated with a workflow initiated by an agent associated with a live conversation.
 29. The non-transitory, computer-readable storage medium of claim 23, wherein analyzing the imported transcript comprises making a prediction regarding a recommended workflow to initiate in response to one or more of the statements in the transcript.
 30. The non-transitory, computer-readable storage medium of claim 23, wherein analyzing the imported transcript comprises identifying a handoff point between the bot and an agent associated with a live conversation.
 31. The non-transitory, computer-readable storage medium of claim 23, further comprising instructions executable to generate an agent dashboard displaying current metrics regarding tracked conversations.
 32. The non-transitory, computer-readable storage medium of claim 23, further comprising instructions executable to provide a filter tool selectable to filter a plurality of tracked conversations based on one or more of the metrics.
 33. The non-transitory, computer-readable storage medium of claim 23, wherein analyzing the imported transcript comprises assigning a sentiment score. 